Every day people hear a host of sounds, some of which are more recognizable than others. In some instances, a person will recognize the source of a particular sound the instant he or she hears it. For example, a dog's owner may easily recognize that the source of a particular dog bark is his or her own dog. In other instances, a person may be less certain as to the source of a particular sound. He or she may have some inclination as to what is making a particular sound, but is not certain. There may be other sounds being made simultaneously with the sound in question, making it more difficult to truly discern the source of the sound of interest. In still other instances, a person may be perplexed as to the source of a particular sound. In instances in which a person does not know, or is at least unsure, of the source of a particular sound, it can be useful for that person to have assistance in identifying the source of the sound. Aside from putting that person at ease by providing an answer to an unknown, allowing a person to identify a source of a sound can allow the person to take any actions that may be advisable in light of knowing the source of the sound. For example, once a person is able to identify the sound of police car siren, the person can take action to move out of the way so that the person does not obstruct the path of the police car.
Individuals with hearing impairment or hearing loss is a segment of the population that in particular can benefit from sound monitoring systems, devices, and methods that enhance the detection, recognition, and identification of sounds. People with hearing loss can often endure specific stress and risk related to their reduced capacity to be alerted of important and in some instances life-threatening sounds. They may not hear the sounds that can prevent injury or neglect, such as a breaking glass, a knock on the door, a fire-alarm, or the siren of an approaching emergency vehicle.
Conventional systems, devices, and methods that are known in the art and directed toward alerting individuals with hearing impairment about the activation of an emergency alarm are designed for integration in emergency alert systems incorporated into buildings with a central alarm. These systems, and the personal alert systems utilized in hospitals, are limited in so far as they do not include sound recognition capabilities that are operable on a user's mobile device, do not classify and identify sounds according to audible and non-audible data in the environment in which the sound occurs, do not incorporate an adaptive learning inferential engine to enhance machine learning about new incoming sounds, and do not increase the efficiency of sound recognition utilizing an open sourced database of sound events. Further, to the extent mobile applications and other systems, devices, and methods exist for the purposes of identifying a sound event, such as identifying a particular song, these mobile applications, systems, devices, and methods often require a lengthy amount of that sound event to be played before it can be identified, and the ways the sound event are identified are limiting. Additionally, existing systems, devices, and methods are limited in that they are not generally able to identify multiple sound events simultaneously, or even near simultaneously.
Accordingly, there is a need for systems, devices, and methods that are able to identify the source of a sound in real time based on a very small sample size of that sound despite background or extraneous sounds or noise, and which are also able to identify the sources of multiple sounds near simultaneously.